US 9550987B2
· Green et al.
· 2017
[cited by applicant]
US 9593338B2
· Liu et al.
· 2017
[cited by applicant]
US 20170175111A1
· Green et al.
· 2017
[cited by applicant]
EP 2221371A1
· 2010
[cited by applicant]
WO WO2004046321A2
· 2004
[cited by applicant]
WO WO2006088165A1
· 2006
[cited by applicant]
WO WO2009066758A1
· 2009
[cited by applicant]
WO WO2014074648A2
· 2014
[cited by applicant]
WO WO2016011089A1
· 2016
[cited by applicant]
WO WO2017205668A1
· 2017
[cited by examiner]
WO WO2019166816A1
· 2019
[cited by applicant]
ZNF175 [online]. GTEX Portal; 2025 [retrieved on Jul. 16, 2025]. Retrieved from the Internet: https://gtexportal.org/home/gene/ZNF17 (Year: 2025).
[cited by examiner]
Rakhra G and Rakhra G. Zinc finger proteins: insights into the transcriptional and post transcriptional regulation of immune response. Mol Biol Rep. Jul. 2021;48(7):5735-5743. Epub Jul. 24, 2021 (Year: 2021).
[cited by examiner]
Expression of ZNF175 in cancer [online]. Human Protein Atlas; 2025 [retrieved Jul. 17, 2025]. Retrieved from the Internet: https://www.proteinatlas.org/ENSG00000105497-ZNF175/cancer (Year: 2025).
[cited by examiner]
Goodman et al., Causes and effects of N-terminal codon bias in bacterial genes. Science. Oct. 25, 2013;342(6157):475-9. doi: 10.1126/science.1241934. Epub Sep. 26, 2013.
[cited by applicant]
Meyer, The role of mRNA structure in bacterial translational regulation. Wiley Interdiscip Rev RNA. Jan. 2017;8(1). doi: 10.1002/wrna.1370. Epub Jun. 14, 2016.
[cited by applicant]
Sauerwine et al., Kinetic Monte Carlo method applied to nucleic acid hairpin folding. Phys Rev E Stat Nonlin Soft Matter Phys. Dec. 2011;84(6 Pt 1):061912. doi: 10.1103/PhysRevE.84.061912. Epub Dec. 19, 2011.
[cited by applicant]
Zuallaert et al., Interpretable convolutional neural networks for effective translation initiation site prediction. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2017:1233-1237. doi: 10.11…
[cited by applicant]
Invitation to Pay Additional Fees mailed Aug. 27, 2021 for Application No. PCT/US2020/064695.
[cited by applicant]
International Search Report and Written Opinion mailed Nov. 22, 2021 for Application No. PCT/US2020/064695.
[cited by applicant]
International Preliminary Report on Patentability mailed Jun. 23, 2022 for Application No. PCT/US2020/064695.
[cited by applicant]
Alley et al., Unified rational protein engineering with sequence-only deep representation learning. bioRxiv. Mar. 26, 2019. doi: 10.1101/589333. 52 pages.
[cited by applicant]
Angenent-Mari et al., A deep learning approach to programmable RNA switches. Nat Commun. Oct. 7, 2020;11(1):5057. doi: 10.1038/s41467-020-18677-1.
[cited by applicant]
Angermueller et al., Deep learning for computational biology. Mol Syst Biol. Jul. 29, 2016;12(7):878. doi: 10.15252/msb.20156651.
[cited by applicant]
Aoki et al., Convolutional neural networks for classification of alignments of non-coding RNA sequences. Bioinformatics. Jul. 1, 2018;34(13):i237-i244. doi: 10.1093/bioinformatics/bty228.
[cited by applicant]
Ausländer et al., Programmable single-cell mammalian biocomputers. Nature. Jul. 5, 2012;487(7405):123-7. doi: 10.1038/nature11149. Abstract only.
[cited by applicant]
Babendure et al., Control of mammalian translation by mRNA structure near caps. RNA. May 2006;12(5):851-61. Epub Mar. 15, 2006.
[cited by applicant]
Badelt et al., Thermodynamic and kinetic folding of riboswitches. Methods Enzymol. 2015;553:193-213. doi: 10.1016/bs.mie.2014.10.060. Epub Feb. 12, 2015.
[cited by applicant]
Baek et al., LncRNAnet: long non-coding RNA identification using deep learning. Bioinformatics. Nov. 15, 2018;34(22):3889-3897. doi: 10.1093/bioinformatics/bty418.
[cited by applicant]
Bailey et al., Dreme: motif discovery in transcription factor ChIP-seq data. Bioinformatics. Jun. 15, 2011;27(12):1653-9. doi: 10.1093/bioinformatics/btr261. Epub May 4, 2011.
[cited by applicant]
Barrick et al., Quantitative analysis of ribosome binding sites in
[cited by applicant]
Bashor et al., Using engineered scaffold interactions to reshape Map kinase pathway signaling dynamics. Science. Mar. 14, 2008;319(5869):1539-43. doi: 10.1126/science.1151153. Abstract only.
[cited by applicant]
Bonnet et al., Amplifying genetic logic gates. Science. May 3, 2013;340(6132):599-603. doi: 10.1126/science.1232758. Epub Mar. 28, 2013. Abstract only.
[cited by applicant]
Borujeni et al., Precise quantification of translation inhibition by mRNA structures that overlap with the ribosomal footprint in N-terminal coding sequences. Nucleic Acids Res. May 19, 2017;45(9):5437-5448. doi: 10.109…
[cited by applicant]
Borujeni et al., Translation Initiation is Controlled by RNA Folding Kinetics via a Ribosome Drafting Mechanism. J Am Chem Soc. Jun. 8, 2016;138(22):7016-23. doi: 10.1021/jacs.6b01453. Epub May 26, 2016.
[cited by applicant]
Borujeni et al., Translation rate is controlled by coupled trade-offs between site accessibility, selective RNA unfolding and sliding at upstream standby sites. Nucleic Acids Res. Feb. 2014;42(4):2646-59. doi: 10.1093/n…
[cited by applicant]
Callura et al., Genetic switchboard for synthetic biology applications. Proc Natl Acad Sci U S A. Apr. 10, 2012;109(15):5850-5.
[cited by applicant]
Camacho et al., Next-Generation Machine Learning for Biological Networks. Cell. Jun. 14, 2018;173(7):1581-1592. doi: 10.1016/j.cell.2018.05.015. Epub Jun. 7, 2018.
[cited by applicant]
Cameron et al., A brief history of synthetic biology. Nat Rev Microbiol. May 2014;12(5):381-90. doi: 10.1038/nrmicro3239. Epub Apr. 1, 2014. Abstract only.
[cited by applicant]
Canton et al., Refinement and standardization of synthetic biological parts and devices. Nat Biotechnol. Jul. 2008;26(7):787-93. doi: 10.1038/nbt1413. Abstract only.
[cited by applicant]
Chuai et al., DeepCRISPR: optimized CRISPR guide RNA design by deep learning. Genome Biol. Jun. 26, 2018;19(1):80. doi: 10.1186/s13059-018-1459-4. 18 pages.
[cited by applicant]
Culler et al., Reprogramming cellular behavior with RNA controllers responsive to endogenous proteins. Science. Nov. 26, 2010;330(6008):1251-5. doi: 10.1126/science.1192128.
[cited by applicant]
Daniel et al., Synthetic analog computation in living cells. Nature. May 30, 2013;497(7451):619-23. doi: 10.1038/nature12148. Epub May 15, 2013. Abstract only.
[cited by applicant]
Danino et al., A synchronized quorum of genetic clocks. Nature. Jan. 21, 2010;463(7279):326-30. doi: 10.1038/nature08753.
[cited by applicant]
Delebecque et al., Organization of intracellular reactions with rationally designed RNA assemblies. Science. Jul. 22, 2011;333(6041):470-4. doi: 10.1126/science.1206938. Epub Jun. 23, 2011. Abstract only.
[cited by applicant]
Dhawan et al., Pan-cancer characterisation of microRNA across cancer hallmarks reveals microRNA-mediated downregulation of tumour suppressors. Nat Commun. Dec. 7, 2018;9(1):5228. doi: 10.1038/s41467-018-07657-1.
[cited by applicant]
Dirks et al., Paradigms for computational nucleic acid design. Nucleic Acids Res. Feb. 27, 2004;32(4):1392-403. doi: 10.1093/nar/gkh291.
[cited by applicant]
Elowitz et al., A synthetic oscillatory network of transcriptional regulators. Nature. Jan. 20, 2000;403(6767):335-8. Abstract only.
[cited by applicant]
Fiannaca et al., nRC: non-coding RNA Classifier based on structural features. BioData Min. Aug. 1, 2017;10:27. doi: 10.1186/s13040-017-0148-2.
[cited by applicant]
Frosst et al., Distilling a Neural Network Into a Soft Decision Tree. Google Brain Team. arXiv Preprint. Nov. 27, 2017. arXiv:1711.09784v1 [cs.LG]. 8 pages.
[cited by applicant]
Gardner et al., Construction of a genetic toggle switch in
[cited by applicant]
Grabow et al., RNA modularity for synthetic biology. F1000Prime Rep. Nov. 1, 2013;5:46. doi: 10.12703/P5-46. eCollection 2013.
[cited by applicant]
Green et al., Complex cellular logic computation using ribocomputing devices. Nature. Aug. 3, 2017;548(7665):117-121. doi: 10.1038/nature23271. Epub Jul. 26, 2017.
[cited by applicant]
Green et al., Toehold switches: de-novo-designed regulators of gene expression. Cell. Nov. 6, 2014;159(4):925-39. doi: 10.1016/j.cell.2014.10.002. Epub Oct. 23, 2014.
[cited by applicant]
Hunt et al., Ensembl variation resources. Database (Oxford). Jan. 1, 2018;2018:bay119. doi: 10.1093/database/bay119.
[cited by applicant]
Isaacs et al., Engineered riboregulators enable post-transcriptional control of gene expression. Nat Biotechnol. Jul. 2004;22(7):841-7. Epub Jun. 20, 2004.
[cited by applicant]
Isaacs et al., RNA synthetic biology. Nat Biotechnol. May 2006;24(5):545-54. doi: 10.1038/nbt1208.
[cited by applicant]
Jäschke, Genetically encoded RNA photoswitches as tools for the control of gene expression. FEBS Lett. Jul. 16, 2012;586(15):2106-11. doi: 10.1016/j.febslet.2012.05.040. Epub May 31, 2012.
[cited by applicant]
Jurtz et al., An introduction to deep learning on biological sequence data: examples and solutions. Bioinformatics. Nov. 15, 2017;33(22):3685-3690. doi: 10.1093/bioinformatics/btx531.
[cited by applicant]
Khalil et al., A synthetic biology framework for programming eukaryotic transcription functions. Cell. Aug. 3, 2012;150(3):647-58.
[cited by applicant]
Kim et al., Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nat Biotechnol. Mar. 2018;36(3):239-241. doi: 10.1038/nbt.4061. Epub Jan. 29, 2018.
[cited by applicant]
Kim et al., De-Novo-Designed Translation-Repressing Riboregulators for Multi-Input Cellular Logic. Nat Chem Biol. Dec. 2019;15(12):1173-82. doi:10.1038/s41589-019-0388-1. Author Manuscript.
[cited by applicant]
Kim et al., Modulating Responses of Toehold Switches by an Inhibitory Hairpin. ACS Synth Biol. Mar. 15, 2019;8(3):601-605. doi: 10.1021/acssynbio.8b00488. Epub Feb. 15, 2019.
[cited by applicant]
Koo et al., Representation learning of genomic sequence motifs with convolutional neural networks. PLoS Comput Biol. Dec. 19, 2019;15(12):e1007560. doi: 10.1371/journal.pcbi.1007560.
[cited by applicant]
Krishnamurthy et al., Tunable Riboregulator Switches for Post-transcriptional Control of Gene Expression. ACS Synth Biol. Dec. 18, 2015;4(12):1326-34. doi: 10.1021/acssynbio.5b00041. Epub Jul. 27, 2015. 31 pages.
[cited by applicant]
Kudla et al., Coding-sequence determinants of gene expression in
[cited by applicant]
Lebars et al., LNA derivatives of a kissing aptamer targeted to the trans-activating responsive RNA element of HIV-1. Blood Cells, Molecules and Diseases. 2007;38:204-9.
[cited by applicant]
Liu et al., An adaptor from translational to transcriptional control enables predictable assembly of complex regulation. Nat Methods. Nov. 2012;9(11):1088-94. doi: 10.1038/nmeth.2184. Epub Sep. 30, 2012. Abstract only.
[cited by applicant]
Liu et al., Prediction of Long Non-Coding RNAs Based on Deep Learning. Genes (Basel). Apr. 3, 2019;10(4):273. doi: 10.3390/genes10040273.
[cited by applicant]
Lorenz et al., ViennaRNA Package 2.0. Algorithms Mol Biol. Nov. 24, 2011;6:26. doi: 10.1186/1748-7188-6-26.
[cited by applicant]
Lucks et al., Versatile RNA-sensing transcriptional regulators for engineering genetic networks. Proc Natl Acad Sci U S A. May 24, 2011;108(21):8617-22. doi: 10.1073/pnas.1015741108. Epub May 9, 2011.
[cited by applicant]
Luo et al., Prediction of activity and specificity of CRISPR-Cpfl using convolutional deep learning neural networks. BMC Bioinformatics. Jun. 13, 2019;20(1):332. doi: 10.1186/s12859-019-2939-6. 10 pages.
[cited by applicant]
Ma et al., Low-cost detection of norovirus using paper-based cell-free systems and synbody-based viral enrichment. Synth Biol (Oxf). 2018;3(1):ysy018. doi: 10.1093/synbio/ysy018. Epub Sep. 19, 2018. 11 pages.
[cited by applicant]
Matthews et al., Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J Mol Biol. May 21, 1999;288(5):911-40. Abstract only.
[cited by applicant]
Moon et al., Genetic programs constructed from layered logic gates in single cells. Nature. Nov. 8, 2012;491(7423):249-53.
[cited by applicant]
Mutalik et al., Rationally designed families of orthogonal RNA regulators of translation. Nat Chem Biol. Mar. 25, 2012;8(5):447-54. doi: 10.1038/nchembio.919.
[cited by applicant]
Narita et al., Cis-regulatory hairpin-shaped mRNA encoding a reporter protein: catalytic sensing of nucleic acid sequence at single nucleotide resolution. Nat Protoc. 2007:2(5):1105-16. Epub May 3, 2007.
[cited by applicant]
Narita et al., Highly sensitive genotyping using artificial riboregulator system. Nucleic Acids Symp Ser No. 49. 2005;271-2.
[cited by applicant]
Oberacker et al., Bio-On-Magnetic-Beads (BOMB): Open platform for high-throughput nucleic acid extraction and manipulation. PLoS Biol. Jan. 10, 2019;17(1):e3000107. doi: 10.1371/journal.pbio.3000107.
[cited by applicant]
Pardee et al., Paper-based synthetic gene networks. Cell. Nov. 6, 2014;159(4):940-54 and Supplemental Info. doi: 10.1016/j.cell.2014.10.004. Epub Oct. 23, 2014. 22 pages.
[cited by applicant]
Pardee et al., Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components. Cell. May 19, 2016;165(5):1255-1266 and Supplemental Info. doi: 10.1016/j.cell.2016.04.059. Epub May 6, 2016. 23 pages.
[cited by applicant]
Qian et al., Neural network computation with DNA strand displacement cascades. Nature. Jul. 20, 2011;475(7356):368-72.
[cited by applicant]
Qian et al., Scaling up digital circuit computation with DNA strand displacement cascades. Science. Jun. 3, 2011;332(6034):1196-201.
[cited by applicant]
Reeve et al., Predicting translation initiation rates for designing synthetic biology. Front Bioeng Biotechnol. Jan. 20, 2014;2:1. doi: 10.3389/fbioe.2014.00001.
[cited by applicant]
Rinaudo et al., A universal RNAi-based logic evaluator that operates in mammalian cells. Nat Biotechnol. Jul. 2007;25(7):795-801. Epub May 21, 2007.
[cited by applicant]
Rodrigo et al., De novo automated design of small RNA circuits for engineering synthetic riboregulation in living cells. Proc Natl Acad Sci U S A. Sep. 18, 2012;109(38):15271-6. Epub Sep. 4, 2012.
[cited by applicant]
Ruder et al., Synthetic biology moving into the clinic. Science. Sep. 2, 2011;333(6047):1248-52. doi: 10.1126/science.1206843. Abstract only.
[cited by applicant]
Salis et al., Automated design of synthetic ribosome binding sites to control protein expression. Nat Biotechnol. Oct. 2009;27(10):946-50. doi: 10.1038/nbt.1568. Epub Oct. 4, 2009.
[cited by applicant]
Sando et al., Doubly catalytic sensing of HIV-1-related CCR5 sequence in prokaryotic cell-free translation system using riboregulator-controlled luciferase activity. J Am Chem Soc. 2005;127:5300-1.
[cited by applicant]
Simonyan et al., Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. Visual Geometry Group, University of Oxford. arXiv Preprint. 2013. arXiv:1312.6034v2 [cs.CV]. 8 pages.
[cited by applicant]
Takahashi et al., A low-cost paper-based synthetic biology platform for analyzing gut microbiota and host biomarkers. Nat Commun. Aug. 21, 2018;9(1):3347. doi: 10.1038/s41467-018-05864-4.
[cited by applicant]
Takahashi et al., A modular strategy for engineering orthogonal chimeric RNA transcription regulators. Nucleic Acids Res. Aug. 2013;41(15):7577-88.
[cited by applicant]
Tamsir et al., Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’. Nature. Jan. 13, 2011;469(7329):212-5. doi: 10.1038/nature09565. Epub Dec. 8, 2010.
[cited by applicant]
To et al., A comprehensive web tool for toehold switch design. Bioinformatics. Aug. 15, 2018;34(16):2862-2864. doi: 10.1093/bioinformatics/bty216.
[cited by applicant]
Vimberg et al., Translation initiation region sequence preferences in
[cited by applicant]
Wainberg et al., Deep learning in biomedicine. Nat Biotechnol. Oct. 2018;36(9):829-838. doi: 10.1038/nbt.4233. Epub Sep. 6, 2018.
[cited by applicant]
Webb, Deep learning for biology. Nature. Feb. 2018;554(7693):555-557. doi: 10.1038/d41586-018-02174-z.
[cited by applicant]
Win et al., Higher-order cellular information processing with synthetic RNA devices. Science. Oct. 17, 2008;322(5900):456-60. doi: 10.1126/science.1160311.
[cited by applicant]
Xie et al., Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science. Sep. 2, 2011;333(6047):1307-11. doi: 10.1126/science.1205527. Abstract only.
[cited by applicant]
Yang et al., STAT3 overexpression promotes metastasis in intrahepatic cholangiocarcinoma and correlates negatively with surgical outcome. Oncotarget. Jan. 31, 2017;8(5):7710-7721. doi: 10.18632/oncotarget.13846.
[cited by applicant]
Zadeh et al., Nucleic acid sequence design via efficient ensemble defect optimization. J Comput Chem. Feb. 2011;32(3):439-52. doi: 10.1002/jcc.21633. Epub Aug. 17, 2010. Abstract only.
[cited by applicant]
Zadeh et al., Nupack: Analysis and design of nucleic acid systems. J Comput Chem. Jan. 15, 2011;32(1):170-3. doi: 10.1002/jcc.21596. Abstract only.
[cited by applicant]
Zhang et al., Control of DNA strand displacement kinetics using toehold exchange. J Am Chem Soc. Dec. 2, 2009;131(47):17303-14. doi: 10.1021/ja906987s. Abstract only.
[cited by applicant]
Zhang et al., Dynamic DNA nanotechnology using strand-displacement reactions. Nature Chemistry 3, 103-113 (2011) doi:10.1038/nchem.957. Abstract only.
[cited by applicant]
Zhang et al., Titer: predicting translation initiation sites by deep learning. Bioinformatics. Jul. 15, 2017;33(14):1234-1242. doi: 10.1093/bioinformatics/btx247. 9 pages.
[cited by applicant]
Zhang et el., Function of hexameric RNA in packaging of bacteriophage phi 29 DNA in vitro. Mol Cell. Jul. 1998;2(1):141-7.
[cited by applicant]
Cambray et al., Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in
[cited by applicant]
Groher et al. Tuning the Performance of Synthetic Riboswitches using Machine Learning. ACS Synth Biol. Jan. 18, 2019;8(1):34-44. doi: 10.1021/acssynbio.8b00207. Epub Jan. 8, 2019.
[cited by applicant]
Höllerer et al., Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping. Nat Commun. Jul. 15, 2020;11(1):3551. doi: 10.1038/s41467-020-17222-4.
[cited by applicant]
Kelley et al., Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. Jul. 2016;26(7):990-9. doi: 10.1101/gr.200535.115. Epub May 3, 2016.
[cited by applicant]
Kinney et al., Massively Parallel Assays and Quantitative Sequence-Function Relationships. Annu Rev Genomics Hum Genet. Aug. 31, 2019:20:99-127. doi: 10.1146/annurev-genom-083118-014845. Epub May 15, 2019.
[cited by applicant]
Kinney et al., Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc Natl Acad Sci U S A. May 18, 2010;107(20):9158-63. doi: 10.1073/pnas.1004290107. Epub May 3, …
[cited by applicant]
Lehr et al., Cell-Free Prototyping of AND-Logic Gates Based on Heterogeneous RNA Activators. ACS Synth Biol. Sep. 20, 2019;8(9):2163-2173. doi: 10.1021/acssynbio.9b00238. Epub Aug. 27, 2019.
[cited by applicant]
Peterman et al., Sort-seq under the hood: implications of design choices on large-scale characterization of sequence-function relations. BMC Genomics. Mar. 9, 2016:17:206. doi: 10.1186/s12864-016-2533-5.
[cited by applicant]
Singh et al. RNA secondary structure prediction using an ensemble of twodimensional deep neural networks and transfer learning. Nat Commun. Nov. 27, 2019;10(1):5407. doi: 10.1038/s41467-019-13395-9.
[cited by applicant]
Valeri et al., Sequence-to-function deep learning frameworks for engineered riboregulators. Nat Commun. Oct. 7, 2020;11(1):5058. doi: 10.1038/s41467-020-18676-2.
[cited by applicant]